remote sensing
Article
Gross Primary Production of a Wheat Canopy Relates
Stronger to Far Red Than to Red Solar-Induced
Chlorophyll Fluorescence
Yves Goulas 1, *, Antoine Fournier 1 , Fabrice Daumard 1 , Sébastien Champagne 1 ,
Abderrahmane Ounis 1 , Olivier Marloie 2 and Ismael Moya 1
1
2
*
LMD/IPSL, CNRS, ENS, PSL Research University, Ecole polytechnique, Université Paris-Saclay,
UPMC Univ Paris 06, Sorbonne Universités, 91128 Palaiseau, France;
[email protected] (A.F.); [email protected] (F.D.); [email protected] (S.C.);
[email protected] (A.O.); [email protected] (I.M.)
Institut National de la Recherche Agronomique, Unité Environnement Méditerranéen et Modélisation des
Agro-Hydrosystèmes, 84914 Avignon, France; [email protected]
Correspondence: [email protected]; Tel.: +33-16-933-5156
Academic Editors: Jose Moreno and Pradad S. Thenkabail
Received: 24 August 2016; Accepted: 8 January 2017; Published: 22 January 2017
Abstract: Sun-induced chlorophyll fluorescence (SIF) is a radiation flux emitted by chlorophyll
molecules in the red (RSIF) and far red region (FRSIF), and is considered as a potential indicator of the
functional state of photosynthesis in remote sensing applications. Recently, ground studies and space
observations have demonstrated a strong empirical linear relationship between FRSIF and carbon
uptake through photosynthesis (GPP, gross primary production). In this study, we investigated the
potential of RSIF and FRSIF to represent the functional status of photosynthesis at canopy level on
a wheat crop. RSIF and FRSIF were continuously measured in the O2 -B (SIF687) and O2 -A bands
(SIF760) at a high frequency rate from a nadir view at a height of 21 m, simultaneously with carbon
uptake using eddy covariance (EC) techniques. The relative fluorescence yield (Fyield) and the
photochemical yield were acquired at leaf level using active fluorescence measurements. SIF was
normalized with photosynthetically active radiation (PAR) to derive apparent spectral fluorescence
yields (ASFY687, ASFY760). At the diurnal scale, we found limited variations of ASFY687 and
ASFY760 during sunny days. We also did not find any link between Fyield and light use efficiency
(LUE) derived from EC, which would prevent SIF from indicating LUE changes. The coefficient
of determination (r2 ) of the linear regression between SIF and GPP is found to be highly variable,
depending on the emission wavelength, the time scale of observation, sky conditions, and the
phenological stage. Despite its photosystem II (PSII) origin, SIF687 correlates less than SIF760 with
GPP in any cases. The strongest SIF–GPP relationship was found for SIF760 during canopy growth.
When canopy is in a steady state, SIF687 and SIF760 are almost as effective as PAR in predicting GPP.
Our results imply some constraints in the use of simple linear relationships to infer GPP from SIF, as
they are expected to be better predictive with far red SIF for canopies with a high dynamic range of
green biomass and a low LUE variation range.
Keywords: sun-induced fluorescence (SIF); red SIF; far red SIF; gross primary production (GPP);
diurnal cycle; photosynthesis efficiency; fluorescence quantum yield; photochemical yield; absorbed
photosynthetically active radiation (APAR); wheat; oxygen absorption band
1. Introduction
Terrestrial vegetation plays a major role at the Earth’s surface by harvesting sunlight and
converting photons’ energy into a redox potential available to the rest of the biosphere. This basic
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process of energy conversion occurs in the antennae pigments of photosystems from the excited
state of chlorophyll a (Chl a). In optimal conditions, the process is highly efficient, although a small
part of the absorbed energy is re-emitted as fluorescence. This emission occurs in two broad bands
in the red and far red parts of the spectrum, with maxima near 685 and 740 nm, respectively [1].
As fluorescence emission competes with photochemical conversion and heat dissipation of the excess
energy, chlorophyll fluorescence (ChlF) has long been considered as a convenient non-intrusive
probe to assess photosynthetic processes in chloroplasts and leaves (for a comprehensive review,
see [2–4]). However, assessment of ChlF at larger scales has long been limited by the power
of light sources that can be used to excite fluorescence from remote distances [5]. In the last
decade, new methods for the remote sensing of ChlF at canopy level were developed to disentangle
sun-induced fluorescence (SIF) from reflected light. Shortly, these methods take advantage of narrow
absorption features of the solar spectrum—the so-called Fraunhofer lines—in which incident radiation
is significantly reduced [6,7]; for a review, see [8]. As vegetation fluorescence is added to reflected
light, it induces a small but detectable alteration of the absorption features, allowing the quantification
of fluorescence. This “in-filling” approach has been successfully applied in the atmospheric absorption
bands (namely, O2 -A and O2 -B) or in the solar Fraunhofer lines to detect vegetation fluorescence from
various platforms at ground level [6,9–12], from an airborne platform [13,14], from an unmanned aerial
vehicle (UAV) [15], and from space [16,17]. Recently, the European Space Agency (ESA) selected the
FLEX (FLuorescence EXplorer) mission to be the 8th Earth Explorer in the framework of the Living
Planet Program [18].
Using data from the GOSAT-TANSO (Greenhouse gases Observing SATellite-Thermal And
Near-infrared Sensor for carbon Observation) instrument, a linear relationship between far red
SIF (FRSIF) and gross primary production (GPP) was found on a monthly or yearly basis [16,19].
On a monthly basis, FRSIF from croplands in the US corn-belt strongly correlated with GPP estimated
from flux towers, but deviated from model-based GPP. Hence, agricultural productivity is suspected to
be severely underestimated by global GPP models [20]. According to these observations, SIF appears
as a powerful indicator that can help to improve the assessment of plant productivity on a global basis.
However, a better understanding of the complex interactions between the factors that contribute to
canopy fluorescence is required to go beyond a simple empirical observation of an association between
GPP and fluorescence radiance. So far, ChlF has been extensively studied at the chloroplast or leaf
level, but observation of ChlF at the canopy level and its relationship with photosynthesis in natural
conditions is still in its infancy [21]. In this context, outdoor ground studies based on continuous
ChlF measurements over well-characterized ecosystems are essential experimental tools to capture
the complex dynamic of SIF at both diurnal and seasonal scales. Using a high spectral resolution
spectrometer, Rossini et al. [22] found a strong linear relationship (r2 = 0.80) between SIF at 760 nm
measured in the O2 -A band (SIF760) and GPP measured with eddy covariance (EC) techniques on
a rice crop in an analysis over two complete vegetative cycles. This value of the correlation coefficient
is close to the value found from space data in [16]. However, in their study, fluorescence measurements
were acquired only at solar noon, and no information is provided about the diurnal dynamic of
fluorescence and its interaction with GPP. In another study performed at ground level on a maize crop
over four seasonal cycles, a much lower correlation between SIF760 and GPP was found (r2 = 0.30) [23],
which indicates that the correlation between SIF and GPP is subjected to changes, while the factors that
control these variations remain unknown. Damm et al. [24] analyzed the full diurnal cycle of SIF and
GPP, but their investigation was limited to a short period in the season, with no major phenological
vegetation changes. In another study, the relationship between airborne SIF and GPP was found to
be asymptotic and ecosystem-specific [25]. Spaceborne SIF is also used to constrain process-based
vegetation models like SCOPE [26], with better simulations of GPP than other approaches based
on vegetation indices [27,28]. However, the factors that drive the SIF–GPP relationship in different
experimental conditions according, for example, to irradiance or phenology remains unclear, as studies
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on this topic are still rare. More specifically, the question remains as to what extent this relationship is
due to absorbed photosynthetically active radiation (APAR) and/or light use efficiency (LUE) [29].
Moreover, most studies are limited to far red SIF (e.g., SIF760, measured in the O2 -A band at
760 nm), and very little information is available on the diurnal and seasonal dynamics of red SIF
(RSIF, e.g., SIF687, measured in the O2 -B band at 687 nm) [23]. Compared to far red fluorescence,
red fluorescence—produced dominantly by photosystem II (PSII)—is the most variable part of ChlF [30].
Hence, RSIF would be a preferential choice to assess physiologically-induced ChlF variations, and the
detection of both peaks would provide complementary information on photosystem I (PSI) and
PSII emissions [31]. In this study, we used the field platform for continuous measurements of
fluorescence [32] located in Avignon, France to investigate both diurnal and seasonal dynamics of RSIF
and FRSIF over a wheat crop during a complete vegetative cycle. SIF was continuously measured
and retrieved from the O2 -B (SIF687) and O2 -A (SIF760) bands. Carbon fluxes were simultaneously
monitored using eddy covariance techniques [33]. In most previous ground studies, SIF measurements
were taken at a very low altitude (around 1 m above the canopy, see [22–24]). These experimental
conditions are not truly representative of a remote sensing viewing configuration, because it implies
either a limited sampled area (which is not representative of the whole canopy), or a large field of view,
which encompasses different viewing angles. Here, we performed nadir measurements from the top
of a crane at an altitude of 21 m with a relatively narrow low field of view (5◦ ) to limit the extent of
the viewing angles. Fluorescence was also assessed at leaf level using active measurements to derive
information on fluorescence and photochemical yields.
The aims of the study are:
-
to investigate both diurnal and seasonal dynamics of RSIF and FRSIF during a complete vegetative
cycle of a crop,
to analyze the relationship between SIF and GPP according to emission wavelength, time scale,
and phenological stage,
to investigate the factors that potentially control the relationship between SIF and GPP
using independent measurements of APAR on one hand, and of fluorescence and photochemical
yield by active methods on the other hand.
2. Materials and Methods
2.1. Experimental Site
The study was conducted with the experimental field platform designed for the continuous
measurement of vegetation fluorescence at the “Institut National de la Recherche Agronomique”
(INRA) site located in Montfavet near Avignon, France (43◦ 550 3.2400 N, 4◦ 520 46.6000 E). The site
is equipped with a movable crane, which can translate between two contiguous field plots of
100 × 200 m2 and 60 × 150 m2 . Additional information on the site and instrumental equipment can be
found in [32]. The two plots were sowed with winter wheat (Triticum turgidum durum, cultivar Daker)
on 19 November 2009, with a plant density of 338 plants/m2 and a distance between rows of 14.5 cm.
Measurements were carried out from 3 March 2010 (day of year (DOY) 62) to 16 June 2010 (DOY 167).
This time period goes roughly from the end of the tillering phase to the end of grain filling (Figure 1).
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0
20
40
Day of year
60
80
100
120
140
160
80
60
60
flowering
40
booting
40
stem
tillering
Canopy height (cm)
Canopy height
Chl content
20
20
0
Chl content (SPAD units)
80
0
01/01
02/01
03/01
04/01
Date
05/01
06/01
Figure 1. Development of the wheat crop during measurement campaign: open circles: vegetation
height; filled squares: leaf chlorophyll (Chl) content. Bars indicate ± one standard deviation; gray zone
indicates measurements campaign.
2.2. Canopy Characterization and Environmental Data
Leaf chlorophyll content was estimated with a chlorophyll meter SPAD-502 (Minolta, Ramsey, NJ,
USA), and is expressed here in SPAD units, which presents a curvilinear relationship with chlorophyll
content and roughly corresponds to it in µg · cm−2 [34]. Leaf fluorescence spectra under natural
light were acquired with a specific laboratory-made device (leaf spectro-fluorometer, LSF) described
elsewhere [13,35]. Briefly, it consists of a focusing optic that projects blue-green filtered sunlight on
the leaf, so that the illumination intensity is equivalent to the outdoor incident photosynthetically
active radiation (PAR). The fluorescence emission spectrum is detected from 660 nm to 800 nm with a
compact spectrometer coupled with a fibre optic. Mean canopy height, fresh and dry above-ground
biomass, and leaf area index (LAI) were measured at periodic intervals during crop development
(Table 1). LAI was assessed using a LI-3100 (Li-Cor, Lincoln, NE, USA) area meter. Incident PAR was
measured at the same sampling rate as the vegetation radiance acquired by TriFLEX (see Section 2.3.1)
with a quantum sensor (JYP 1000, SDEC, Reignac sur Indre, France). Half-hourly diffuse fraction
of incident PAR was measured using a BF3 light sensor (Delta-T Devices, Cambridge, UK) and air
temperature with a HPM35D sensor (Vaisala, Helsinki, Finland) (see Figure 2).
Table 1. Height, leaf area index (LAI), and aerial biomass of the crop at different dates during the
measurement campaign.
Date
2 March
24 March
14 April
7 May
26 May
9 June
Day of Year
Height (cm)
LAI (m2 · m−2 )
Fresh biomass (kg · m−2 )
Dry biomass (kg · m−2 )
34
8.6
0.15
0.091
0.018
83
18
0.89
0.58
0.116
104
40
3.32
2.13
0.384
127
69
6.88
4.26
1.02
146
86
5.74
4.32
1.51
160
86
1.07
3.50
1.67
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1500
1000
500
0
40
30
20
10
20
0
0
60
80
100
120
Day of year
140
Temperature (°C)
precipitations
(mm)
-2
µmoles m s
-1
2000
160
Figure 2. Time course of environmental conditions during the experiment: photosynthetically
active radiation (PAR, top, µmol · m−2 · s−1 ), air temperature (middle, ◦ C), and daily precipitations
(bottom, mm).
2.3. Passive Remote Sensing of SIF and Reflectance
2.3.1. Instrumental Setup and Retrieval of Fluorescence
Vegetation radiance was recorded with the TriFLEX instrument described in [32].The instrument
was fixed at the top of the crane to observe vegetation from nadir. At the working height (21 m), the
diameter of the observed area on top of the canopy was about 2 m. Given the crop homogeneity,
this measuring configuration can be considered as representative of true remote sensing conditions.
Briefly, TriFLEX is a fluorosensor that uses two identical spectro-radiometers (HR2000+, 10 µm entrance
slit, full width at half-maximum (FWHM) 0.4 nm at 687 nm and 0.5 nm at 760 nm, Ocean Optics,
Dudenin, FL, USA) to simultaneously record the vegetation radiance and irradiance spectra in the
chlorophyll emission band from 630 nm to 815 nm with a 0.09 nm/pixel sampling interval. With this
setting, the time delay between vegetation and irradiance spectrum is reduced, enabling kinetic
measurements of SIF. A third spectro-radiometer (HR2000+, 50 µm entrance slit, FWHM 2 nm)
alternatively measured vegetation radiance and solar irradiance on a broader spectral range from
300 nm to 900 nm. Irradiance spectra were assessed by measuring radiance over a white reference
surface made with a frosted polyvinyl chloride panel. The reflectance spectrum of the reference
panel was determined in the laboratory against a reflectance standard (Spectralon, Labsphere, North
Dutton, NH, USA), and further checked at the end of the measurements campaign to account for
possible shifts induced by prolonged outdoors conditions. Each spectro-radiometer was connected
to a fibre optic, which looks down to the vegetation at nadir with a field of view corresponding to
a measured area of 2 m diameter. Vegetation and reference spectra were atmospherically corrected for
the difference in altitude between ground and the reference panel using MODTRAN 4 [36]. The signal
acquired on the vegetation was corrected to compensate for the absorption of the atmosphere along
the path from vegetation to sensor, and the signal acquired on the reference board was corrected for
the lack of absorption of incident light from the reference altitude to the ground. The two measured
signals were corrected from these effects using MODTRAN 4, which computes the two transmission
factors [37]. The variation of fluorescence flux induced by this atmospheric correction was about 5%
for the O2 -A absorption band and 1.3% for the O2 -B band. Raw data were aggregated to a limited
number of spectral channels designed to capture the essential features of each absorption band (three
channels at 757.86, 760.51, and 770.46 nm for the A band, four channels at 683.14, 685.00, 686.97,
and 697.06 nm for the B band), and were automatically saved on a remote server. Bandwidths of
integration were from 0.1 nm to 0.89 nm, according to channel (see [32] for details). SIF retrieval
was performed with the algorithm proposed in [32] and described in Appendix A . In this method
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(called nFLD hereafter, where n is the number of measuring channels), fluorescence radiance at
a given absorption band (SIF687 in the O2 -B band, SIF760 in the O2 -A band) is retrieved using a
limited number (n) of measuring channels in each O2 band and specific models of fluorescence and
reflectance that apply only in the vicinity of each oxygen absorption band. We used three channels
in the O2 -A band, and a polynomial of degree one to model the reflectance (i.e., a linear model), and
four channels in the O2 -B band with a polynomial of degree two to model reflectance. In Appendix A,
the retrieval errors were assessed on a simulated database of canopy and reference panel radiances
with a known level of fluorescence. The mean retrieval error (SIF bias ), as well as the root mean
square error (SIF RMSE ) computed from retrievals on a set of simulated top of canopy (TOC) radiances
were found to be higher for SIF687 than for SIF760 (−0.038 vs. 0.015 mW · m−2 · sr−1 · nm−1 for
SIF bias , and 0.061 vs. 0.022 mW · m−2 · sr−1 · nm−1 for SIF RMSE , respectively, under a PAR irradiance
of 1400 µmol · m−2 · s−1 (see Appendix A). These error values should be taken into account in the
interpretation of the experimental results after PAR correction.
2.3.2. Derivation of Other Remote Sensing Parameters
Raw fluorescence and other optical data were averaged over 1 min time intervals in order to
reduce the relative random error. This sampling rate allows for the discrimination of rapid changes
in illumination caused by clouds. Wheat has a rather erectophile canopy architecture [38,39], and
erectophile canopies are the most affected by bidirectional effects induced by changes in illumination.
An additional averaging over 30 min was applied in order to synchronize fluorescence data with gas
exchange measurements. We defined an apparent spectral fluorescence yield (ASFY) of the canopy
(ASFY687, ASFY760) by dividing SIF expressed in quanta units (µmol · m−2 · sr−1 · nm−1 ) by PAR
(µmol · m−2 · s−1 ) [9,40]. The factor π is used here to convert radiance units into irradiance units.
Hence, ASFY is equivalent to a spectral density with units in nm−1 .
ASFYx[nm−1 ] =
π × SIFx[µmol · m−2 · sr−1 · nm−1 ]
; x ∈ {687, 760}
PAR[µmol · m−2 · s−1 ]
(1)
In a similar way, we defined the true spectral fluorescence yield (SFY) as:
SFYx[nm−1 ] =
π × SIFx[µmol · m−2 · sr−1 · nm−1 ]
; x ∈ {687, 760}
APAR[µmol · m−2 · s−1 ]
(2)
We also computed the Normalized Difference Vegetation Index (NDVI) from spectroscopic data as:
NDVI =
ρ755 − ρ685
ρ755 + ρ685
(3)
where ρ685 and ρ755 are the vegetation reflectance at 685 nm and 755 nm, respectively.
2.4. Measurements of Canopy Transmittance and Absorbed PAR
Canopy transmittance was measured with a laboratory-made system which consists of ten
quantum sensors (spectral bandwidth 400–700 nm) randomly distributed under the canopy at ground
level. The transmitted irradiance IT was computed as the mean irradiance on the ten sensors averaged
over time intervals of 30 min. An eleventh sensor measured the incident PAR at the top of the canopy
in the same spectral band. APAR and the fraction of absorbed PAR (fAPAR) were computed from
transmitted irradiance as:
I
fAPAR = 0.96 × (1 − T )
(4)
PAR
APAR = fAPAR × PAR
(5)
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The factor 0.96 results from the regression model between the true fAPAR (which requires the
measurement of both transmitted and reflected light), and the fAPAR obtained here, which is deduced
from transmitted light only [41].
2.5. Measurements of CO2 Fluxes
The Avignon site is part of the CarboEurope-IP Regional Experiment [42] and the CarboEurope-IP
Ecosystem Component. CO2 fluxes were measured continuously using the eddy covariance (EC)
technique at 30 min intervals by means of a Young 81000 3D sonic anemometer (Young, Traverse City,
MI, USA) and a LI-7500 open path CO2 /H2 O analyzer (Li-Cor, Lincoln, NE, USA). We used Level 4
(i.e., quality-checked and gap-filled) data products from the CarboEurope database [33]. The footprint
analysis suggests that—on average—85–90% of the fluxes originated from the studied field. Because
EC measures Net Ecosystem Exchange (NEE), which is the algebraic sum of the CO2 fixed by plants
(GPP) and ecosystem respiration (Re), NEE was partitioned to derive GPP. For this purpose, day-time
measurements were fitted to a hyperbolic dependence with PAR according to [43]:
NEE =
a1 × PAR
+ Re
a2 + PAR
(6)
with fitting parameters a1 , a2 , and Re . With this formulation, the estimate of NEE at zero PAR gives the
mean daytime respiration Re , which was subsequently fit to an exponential function of temperature
and extrapolated to daytime temperature. The mean value for Re during the measurement period was
4.6 ± 1.0 µmolC · m−2 · s−1 (1 sd). This is a rather moderate value compared to the total carbon flux.
By subtracting Re from NEE, we computed the half-hourly average of GPP as:
GPP = −NEE + Re
(7)
2.6. Active Fluorometry in the Field
Fluorescence parameters and incident PAR at the leaf level were obtained with a PAM-2000
(Heinz WalzGmbh, Eichenring, Effeltrich, Germany) equipped with a leaf clip holder. Fluorescence
emission was filtered before detection with a RG9 long-pass filter (Schott, Germany, cut-off wavelength
700 nm) and a heat protection filter. The RG9 filter prevents from detecting excitation light (650 nm)
and defines a spectral detection band in the far red, without any sensitivity to the red peak. PAM-2000
measurements were taken at 8:30, 12:00, and 16:00 (Coordinated universal time, UTC). At each
measurement time, around 20 positions on different leaves in the canopy were randomly selected, and
the following measurements were performed at each position pi : (i) incident PAR on the leaf (PARi );
(ii) steady state fluorescence under ambient light (Fsi ); and (iii) maximum fluorescence level under a
saturating light pulse (F 0 mi ). Care was taken to avoid changing the leaf orientation by the measuring
process. From these measurements, the photochemical yield (Φi ) of photosystem II (PSII) was derived
according to [44]:
F 0 mi − Fsi
∆F
Φ Pi =
= 0 i
(8)
F 0 mi
F mi
Leaf measurements taken at a given time were averaged using PARi as weight coefficients. As the
intensity of the pulsed excitation and the configuration of the leaf clip remained constant, Fsi can be
considered as a relative measurement of the apparent fluorescence yield at position pi , which is defined
as the ratio between emitted fluorescence and incident light. Under natural sunlight, fluorescence flux
emitted by each leaf area at position pi can be approximated by PARi ∗ Fsi , if we neglect variations of
apparent yield caused by chlorophyll content and excitation wavelength. This approximation can be
justified by the fact that leaf chlorophyll content is high (i.e., >40 µg · cm−2 ) and that incident radiation
is almost totally absorbed. The fluorescence flux at each individual position sums up to produce the
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total fluorescence flux emitted by the whole leaf area of the canopy. Hence, we can define the mean
apparent fluorescence yield of the total leaf area as:
Fs =
∑i PARi × Fsi
∑i PARi
(9)
Ideally, the whole set of individual measurements should cover exactly the total leaf area of
the canopy. Practically, we only have a subsample of it. However, we assume that the resulting
weighted mean is still an approximation of the true value within the experimental error caused by
imperfect sampling.
Similarly, electrons flux—which varies as the product of Φ P and PAR—sums up on the total leaf
area of the canopy, and we can define the mean photochemical yield (Φ P ) as:
ΦP =
∑i PARi × Φ Pi
∑i PARi
(10)
Here we again assume (as above) that Φ P can be considered as an approximation of the true
canopy photochemical yield, despite the error introduced by under-sampling.
2.7. SIF and GPP Models
Following an efficiency approach largely used for plant productivity and initially proposed by
Monteith [45], SIF can be conceptualized as the product of absorbed photosynthetically active radiation
(APAR), fluorescence yield Fyield(λ), and an escape factor τ (λ) that accounts for the probability of
emitted fluorescence to escape from the canopy and reach the sensor [20]. Fyield(λ) is the light-use
efficiency for fluorescence, and represents the fraction of absorbed PAR photons that are re-emitted
as fluorescence. The factor τ (λ) can be considered as the effective transmittance of the canopy for
fluorescence. Hence, SIF can be written as:
SIF(λ) = APAR × Fyield(λ) × τ (λ)
(11)
On the other hand, GPP can be expressed as [45]:
GPP = APAR × LUE
(12)
where LUE is the light use efficiency, which accounts for the fraction of absorbed photons that drive
carbon fixation. SIF and GPP share a common factor, APAR. Any deviation from a strict proportional
relationship between SIF and GPP is driven by the relationships between the other factors Fyield,
τ, and LUE.
2.8. Statistical Analysis
Statistical analyses (Analysis of variance (ANOVA), Tukey tests, linear regression) have been
performed with the Mathematica 9 software package (Wolfram Research Inc., Champaign, IL, USA)
and the Igor Pro 6 software (Wavemetrics, Lake Oswego, OR, USA).
3. Results
3.1. Daily Cycles of Fluorescence
Illumination conditions in terms of light intensity and incident angle present large variations
during the time course of the day, which can impact light absorption and emission. Figure 3 shows
mean diurnal patterns of top of canopy (TOC) red and far red radiances (L685, L758) and fluorescence
signals (SIF687, SIF760, ASFY687, ASFY760) on sunny days , when the crop has reached a steady
mature state (LAI = 6, see Table 1). PAR showed a typical variation close to a cosine law as a function
Remote Sens. 2017, 9, 97
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of sun zenith angle (Figure 3a). Depending on emission wavelength, SIF exhibited different diurnal
patterns: SIF687 showed an almost linear increase (decrease) with time before (after) solar noon,
while SIF760 had a rather bell-shaped variation, reaching a steady state level around solar noon
(Figure 3c,d). TOC radiances daily cycles were also wavelength-dependent (Figure 3e,f). An interesting
result is that SIF and radiance patterns were quite similar for nearby wavelengths. At wavelengths
strongly absorbed by chlorophyll (i.e., 687 nm for SIF and 685 nm for L), linear variations with time
were observed. At non-absorbed wavelengths (i.e., 760 nm for SIF and 758 nm for L), the diurnal cycle
showed a bell-shaped pattern similar to PAR. Differences in emission wavelengths were also reflected
in the mean diurnal course of reflectances and ASFY (Figure 3g,h). ASFY687 reached its highest level
around noon, while ASFY760 showed a small decrease compared to its morning value.
Diurnal variations of fAPAR—caused, for example, by leaf movements or by the complex
interactions of varying incident angles with canopy structure—would affect ASFY diurnal pattern.
To evaluate possible fAPAR effects on ASFY, we investigated the fAPAR time course by measuring the
transmitted PAR and converted it into fAPAR using Equation (4). During the mature state of the crop,
fAPAR showed only small variations, and a small decrease (<2%) could be observed in the middle of
the day in clear sky conditions.
To better understand the origin of daily changes in ASFY, we compared SIF measurements with
active fluorescence measurements performed at leaf level with the PAM-2000. Active measurements
provide relative changes in fluorescence quantum yield if measurements are taken on the same leaf and
if all experimental parameters are kept constant (e.g., measuring distance, measuring beam intensity).
When comparing different leaves, changes in pigment content can result in an additional dispersion.
Here we compared the PAM-2000 fluorescence level (Fs) between groups of leaves sampled in the
morning and at noon on three different sunny days (Table 2). The analysis was limited to sunny days
to ensure repeatability in light intensity and quality between days. No significant differences in Fs
were observed between groups taken at the same hour on different days. However, a slight but highly
significant decrease (p < 0.01) was observed on six out of nine days when comparing noon groups to
morning groups of leaves (−12% on average). A significant decrease between morning and noon was
also observed on F 0 m and Φ P (−31% and −32% on average, respectively). The decrease in F 0 m shows
that a significant amount of non photochemical quenching was accumulated at noon, which reduced
the photochemical yield of photosystem II. Significant variations of ASFY687 and ASFY760 were also
observed from Table 2 (+12% and −8.7% on average, respectively), but none correspond linearly to the
amount of the decrease in photochemical yield.
During the day, surface irradiance can also present large changes in the fractions of direct and
diffuse light. In order to evaluate the effect of light intensity and directionality on apparent yield,
we compared the SIF–PAR relationship between clear sky and overcast days for different development
stages of the crop (Figure 4). It was found that FRSIF was more responsive than red SIF to crop
development, while it was less responsive to the diffuse fraction of PAR ( f di f f PAR ). This point is
further illustrated by Figure 5, where ASFY is plotted against f di f f PAR for a mature crop only, to avoid
LAI effects on ASFY. It clearly shows than ASFY687 increased with f di f f PAR , while ASFY760 showed
almost no effect (see also Table 3). The transition from clear to overcast sky has two main effects on
irradiance: (i) a decrease in the PAR intensity; and (ii) a change in the incident angle distribution of
the incoming light. We observed only a limited change of Fs at leaf level when light level changed
from 1500 to 500 µmol · m−2 · s−1 (+10%, see Section 3.4). This suggests that irradiance intensity is not
the only factor acting on SIF, and that other factors such as the angular distribution of incoming PAR
should also be considered in the relationship between SIF and PAR.
Remote Sens. 2017, 9, 97
10 of 31
HaL
HbL
SIF687SIF760
PAR Hµmoles m-2 s-1 L
0.50
1500
1000
500
0.45
0.40
0.35
0.30
0
8
10
12
14
HcL
0.6
0.4
0.2
0.0
12
14
HeL
4
2
12
14
16
10
12
14
16
10
12
14
16
10
12
14
16
12
14
16
12
14
16
HdL
1.0
0.5
0.0
16
6
10
1.5
8
L755 HmW m-2 sr-1 nm-1 L
L685 HmW m-2 sr-1 nm-1 L
8
10
8
2.0
SIF760 HmW m-2 sr-1 nm-1 L
SIF687 HmW m-2 sr-1 nm-1 L
0.8
8
0.25
16
100
Hf L
80
60
40
20
0
0
8
10
12
14
16
8
10
25
ASFY760 ´106
ASFY760 ´106
HgL
8
6
4
2
0
20
15
HhL
10
5
8
10
12
14
0
16
8
L
L
0.05
0.50
H jL
HiL
0.45
0.03
Ρ755
Ρ685
0.04
0.02
0.35
0.01
0.00
0.40
8
10
12
14
0.30
16
8
10
50
HkL
40
0.95
fAPAR
GPP Hµmoles m-2 s-1 L
60
30
20
HlL
0.94
10
0
8
10
12
time HUTCL
14
16
0.93
8
10
time HUTCL
Figure 3. Diurnal cycles of optical signals and gross primary production (GPP). Mean of six clear
sky days during the steady mature state of the crop (dark solid line) ± one standard deviation (gray
area). (a) PAR; (b) Fluorescence ratio SIF687/SIF760; (c,d) Fluorescence radiances at 687 nm and
760 nm (SIF687, SIF760); (e,f) Vegetation radiances at 685 and 758 nm (L685, L758); (g,h) Apparent
Spectral Fluorescence Yield (ASFY) at 687 and 760 nm (ASFY687, ASFY760); (i,j), top of canopy (TOC)
reflectance at 685 nm and 755 nm; (k) GPP; (l) fraction of absorbed PAR (fAPAR). Days included in the
analysis are: 05/17, 05/19, 05/21, 05/22, 05/24, 06/04 (day of year (DOY)s 137, 139, 141, 142, 144, 155,
respectively).
Remote Sens. 2017, 9, 97
11 of 31
Table 2. Changes of fluorescence between morning ( 8:00 UTC) and noon ( 12:00 UTC) on sunny days.
Fs: mean leaf fluorescence level measured with a PAM-2000; F0 m: maximum fluorescence level of leaf
upon a saturating pulse; Φ P : leaf photochemical yield; number of samples as indicated; ASFY687 and
ASFY760, apparent spectral fluorescence yield at 687 and 760 nm defined as the ratio between
fluorescence radiance and incident PAR. Mean values between 8:00 and 8:30 (morning) and 12:00
and 12:30 (noon) are reported. Significance levels are indicated: n.s., * p < 0.05, ** p < 0.01, *** p < 0.001.
The measuring distance on the leaf-clip was modified on DOY 149, so Fs and F0 m measurements
between DOYs 119–142 and DOYs 149–156 are not comparable.
PAR
(µmol · m−2 · s−1 )
Fs
(a.u.)
F0 m
(a.u.)
ΦP
ASFY687
(nm−1 10−6 )
ASFY760
(nm−1 10−6 )
2010/04/29 (DOY 119)
morning
Rel. var. noon/morning
1159
(n = 20)
0.377
−14% (***)
(n = 20)
0.6
−25% (***)
(n = 20)
0.367
−25% (***)
(n = 30)
0.37
12% (***)
(n = 30)
1.14
−3% (***)
2010/05/13 (DOY 133)
morning
Rel. var. noon/morning
1230
(n = 20)
0.344
−12% (*)
(n = 20)
0.498
−16% (n.s.)
(n = 20)
0.302
−12% (**)
(n = 30)
0.318
11% (***)
(n = 30)
1.13
−11% (***)
2010/05/17 (DOY 137)
morning
Rel. var. noon/morning
1257
(n = 19)
0.359
−15% (**)
(n = 19)
0.534
−24% (***)
(n = 19)
0.328
−25% (***)
(n = 30)
0.325
15% (***)
(n = 30)
1.08
−3% (*)
2010/05/20 (DOY 140)
morning
Rel. var. noon/morning
1288
(n = 20)
0.347
−17% (**)
(n = 20)
0.513
−21% (n.s.)
(n = 20)
0.321
−11% (***)
(n = 30)
0.341
12% (***)
(n = 30)
1.12
−16% (***)
2010/05/22 (DOY 142)
morning
Rel. var. noon/morning
1189
(n = 17)
0.322
−7% (n.s.)
(n = 17)
0.77
−45% (***)
(n = 17)
0.557
−47% (***)
(n = 30)
0.287
4% (***)
(n = 30)
0.88
−10% (***)
2010/05/29 (DOY 149)
morning
Rel. var. noon/morning
1327
(n = 32)
0.194
−10% (***)
(n = 32)
0.387
−32% (***)
(n = 32)
0.474
−30% (***)
(n = 30)
0.345
6% (***)
(n = 30)
1.04
−5% (**)
2010/06/02 (DOY 153)
morning
Rel. var. noon/morning
1289
(n = 30)
0.196
−4% (n.s.)
(n = 30)
0.443
−39% (***)
(n = 30)
0.526
−45% (***)
(n = 30)
0.3.=35
27% (***)
(n = 30)
0.952
−4% (**)
2010/06/04 (DOY 155)
morning
Rel. var. noon/morning
1291
(n = 30)
0.194
−13% (**)
(n = 30)
0.367
−37% (***)
(n = 30)
0.44
−44% (***)
(n = 30)
0.307
8% (***)
(n = 30)
0.822
−16% (***)
2010/06/05 (DOY 156)
morning
Rel. var. noon/morning
1299
(n = 20)
0.189
−20% (***)
(n = 20)
0.307
−40% (***)
(n = 20)
0.368
−52% (***)
(n = 30)
0.308
14% (***)
(n = 30)
0.751
−10% (***)
0.8
SIF760 HmW m-2 sr-1 nm-1 L
SIF687 HmW m-2 sr-1 nm-1 L
Date
0.6
0.4
0.2
0.0
0
500
1000
1500
-2
PAR Hµmoles m
2000
2.0
1.5
1.0
0.5
0.0
0
500
1000
1500
2000
PAR Hµmoles m-2 s-1 L
-1
s L
LAI
low
Diffuse fraction
low
high
high
low
high
Figure 4. Relationship between PAR and sun-induced fluorescence (SIF) for different development
stages and diffuse fractions of PAR ( f di f f PAR ). Two classes of LAI are represented: low—LAI ranges
from approx. 0.8 to 6 (DOY from 62 to 119, blue and gray points); high—LAI is approx. 6 (DOY from
120 to 157, red and green points). Classes of diffuse fraction of PAR ( f di f f PAR ): low—clear sky days
only, f di f f PAR ranges from 0.15 to 0.5; high— f di f f PAR ≥ 0.9.
11
10
9
8
7
6
5
0.0
12 of 31
ASFY760 ´ 106 Hnm-1 L
ASFY687 ´ 106 Hnm-1 L
Remote Sens. 2017, 9, 97
0.2
0.4
0.6
0.8
1.0
25
20
15
10
0.0
0.2
Diffuse fraction
0.4
0.6
0.8
1.0
Diffuse fraction
Figure 5. Effect of diffuse fraction of PAR on apparent spectral fluorescence yield of canopy
(ASFY687, ASFY760). Only days between DOY 120 (30 Aprill) and DOY 157 (6 June)—when the
crop was in a steady state—are considered here to avoid canopy growth effects.
Table 3. Changes in mean ASFY upon sky conditions. Data taken from Figure 5.
Diffuse fraction of PAR
n samples
Median PAR (µmol · m−2 · s−1 )
Median ASFY687 × 106 (nm−1 )
Median ASFY760 × 106 (nm−1 )
Clear Sky
Overcast
≤0.2
221
1620
6.1
18.7
≥0.9
93
494
9.3
21.1
3.2. Seasonal Patterns of Fluorescence
Figure 6 gives an overview of the time course of the fluorescence signals during the whole
measurements campaign, compared to NDVI, GPP, and fAPAR. NDVI increased at the beginning of
the measurement period from a value of about 0.4–0.45, and reached a steady state level above 0.9
which lasted from DOY 100 to DOY 150, and then decreased slightly at the beginning of the senescent
phase. It should be noted that during the steady state phase of NDVI, the crop still grew up, as can
be seen from canopy height and biomass measurements (Figure 1; Table 1). During the measurement
period, SIF687 varied from 0.2 to 0.8 mW · m−2 · sr−1 · nm−1 , while SIF760 varied from almost 0
to 2 mW · m−2 · sr−1 · nm−1 . SIF760 showed large variations in amplitude at the beginning of the
measurement period during crop growth, even when NDVI reached a steady state level (Figure 6c,d).
A strong decrease of SIF760 was also observed at the end of the experiment, associated with the
senescent period and a decrease of leaves’ chlorophyll content (see Figure 1). Compared to SIF760
(Figure 6c), SIF687 (Figure 6b) showed more limited changes in intensity, despite crop growth and
senescence. These contrasting evolution patterns between fluorescence bands are better evidenced by
the time course of the emission ratio (Figure 6e). SIF687/SIF760 showed large variations during crop
growth and senescence, and remained almost stable with low values between 0.25 and 0.6 during crop
maturity. Normalization of SIF687 and SIF760 with PAR reduced diurnal and day-to-day variations,
as can be seen from the seasonal patterns of ASFY687 and ASFY760 (Figure 6f,g). Again, it can also
be observed that the two fluorescence bands exhibited very different patterns. ASFY687 showed less
variations than ASFY760 during the different phases of the crop development (apart from some peaks
associated with cloudy periods), while ASFY760 showed a regular increase from the beginning of the
experiment and a strong decrease at senescence.
Figure 6 shows that fAPAR increased during the development stage (until DOY 120) and then
remained stable from DOY 120 to DOY 154 at a high level (>0.93) until the beginning of senescence.
During senescence, fAPAR decreased slightly, in association with a concomitant lowering of Chl content
(Figure 1). fAPAR presented daily variations, which were up to 20% at the beginning of the growth
phase (DOY < 110), but were greatly reduced as soon as fAPAR reached its highest values (≥2%).
Remote Sens. 2017, 9, 97
13 of 31
-1
At the seasonal scale, GPP presented a regular increase during crop growth from DOYs 60 to
120, and a strong decrease at senescence. The seasonal patterns of GPP and SIF760 were quite similar,
but large differences were observed between the patterns of GPP and SIF687.
-2
µmoles m s
2000
1500
-1
-1
0.6
0.4
mW m sr nm
0
0.8
-2
1000
500
(b) SIF687
0.2
0.0
2.0
1.5
1.0
0.5
0.0
1.0
-2
-1
mW m sr nm
-1
(a) PAR
(c) SIF760
(d) NDVI
0.5
0.0
4
(e) SIF687/SIF760
3
2
1
0
-6
(f) ASFY687
8
nm
-1
12x10
4
(g)ASFY760
-2
µmoles C m s
-1
nm
-1
0
-6
30x10
25
20
15
10
5
0
50
40
30
20
10
0
1.00
(h) GPP
(i) fAPAR
0.90
0.80
0.70
60
80
100
120
140
160
doy
Figure 6. (a–i) Seasonal time course of main canopy variables (PAR, SIF687, SIF760, Normalized
Difference Vegetation Index (NDVI), SIF687/SIF760, ASFY687, ASFY760, GPP, fAPAR) from growth
phase to senescence. (a–c,h) Sticks indicate 30 min averaged values, squares indicate average over
the day (8:00–15:30 UTC). . (d–g) 30 min averaged values. (i)Squares indicate average over the day
(8:00–15:30 UTC) and error bars indicate maximum and minimum fAPAR values during the course of
the day, .
3.3. Relationship between SIF and GPP
The relationship between SIF and GPP was investigated by linear regression between the
two variables upon different observational cases: (i) the emission wavelength (SIF687 vs. SIF760);
(ii) the time scale of observation (vs. daily averages in the 8:00–15:30 UTC time period) (Figure 7);
(iii) the diffuse fraction of PAR (clear sky vs. all sky conditions); and (iv) the time of observation.
A significant relationship was found between half-hourly values of SIF760 and GPP (r2 = 0.58,
Figure 7b). Using daily instead of half-hourly averages significantly increased the coefficient of
determination (r2 = 0.83, Figure 7d), while the slope of the regression line did not change (17.6 ±
Remote Sens. 2017, 9, 97
14 of 31
0.9 (1sd) (daily) instead of 17.2 ± 0.4 (1sd) µmoles C · J−1 · sr · µm (half-hourly)). On the other hand, the
relationship between half-hourly SIF687 and GPP was strongly reduced compared to SIF760 (r2 = 0.25,
Figure 7a). Using daily averages improved the relationship, but still gave a coefficient of determination
(r2 = 0.34) lower than the corresponding one for SIF760 (Table 4). The relationship between half-hourly
SIF and GPP slightly weakened if we considered measurements under clear sky, except if we considered
only measurements when the sky is clear during the full day (Table 4), where a slight increase of
the coefficient of determination was observed. For daily-integrated measurements, limiting the
observations to clear sky increased r2 for both SIF687 and SIF760. A critical question in SIF remote
sensing is the representativeness of single SIF measurements per day to describe carbon uptake over
a long period of time [16,20]. Sun-synchronous satellites like GOSAT or FLEX can only acquire SIF
once a day. For example, the orbit of FLEX allows a single measurement at 10:00 hours (local time
descending node, LTDN), which varies slightly with latitude. We evaluated the relationship between
SIF measured at a given time in the day and GPP. Two cases were considered for the integration
time of GPP: (i) the instantaneous value, integrated over 30 min at the same time as the fluorescence
measurement; and (ii) the daily integrated value between 8:00 and 15:30. Slight changes in the
coefficient of determination were observed as a function of measurement time, with the strongest
relationship occurring around noon (Figure 8). We therefore evaluated the relationship between
SIF and GPP for SIF measurements at 12:00 according to different sky conditions: (i) clear and
cloudy sky together; (ii) clear sky at the time of SIF measurement; (iii) clear sky during the entire
day of measurement (Table 5). It was found that mixing clear and cloudy sky conditions in the
regression decreased the coefficient of determination. Additionally, the relationship decreased when
we considered instantaneous values of GPP instead of daily integrated ones. The strongest relationship
(r2 = 0.93) was found for SIF760 when we only included data from clear sky days in the regression
computation. This questions the potential of simple linear relationship based on SIF to estimate the
fraction of carbon uptake that takes place during overcast days.
Table 4. Statistics of the linear regression between SIF and GPP as a function of experimental conditions.
Integration time: 30 min, SIF and GPP are averaged over 30 min; 6.5 h, SIF and GPP are averaged
between 8:00 and 15:30 UTC. Sky conditions: All, all measured points are included in the analysis; Clear
sky, clear sky at the measuring time only; Clear sky during the whole day, only data from complete
clear sky days are considered. p-value and t-statistics for the probability of the regression slope to be
zero are indicated.
Integration Time
30 min
30 min
30 min
6.5 h
6.5 h
Sky Conditions
All
Clear Sky
Clear Sky during the Whole Day
All
Clear Sky during the Whole Day
data points
768
313
173
72
17
SIF687
r2
p-value
t-statistics
0.25
<0.001
16.05
0.16
<0.001
7.73
0.33
<0.001
9.21
0.34
<0.001
6.07
0.58
<0.001
4.57
SIF760
r2
p-value
t-statistics
0.58
<0.001
32.46
0.52
<0.001
18.31
0.61
<0.001
16.39
0.83
<0.001
18.28
0.88
<0.001
10.74
Remote Sens. 2017, 9, 97
HaL
50
GPP Hµmoles C m-2 L
GPP Hµmoles C m-2 L
50
15 of 31
40
30
20
10
HbL
40
30
20
10
0
0.0
0.2
0.4
0.6
0
0.0
0.8
0.5
SIF687 HmW m-2 sr-1 nm-1 L
HcL
40
GPP Hµmoles C m-2 L
GPP Hµmoles C m-2 L
40
30
20
10
0
0.0
1.0
1.5
2.0
SIF760 HmW m-2 sr-1 nm-1 L
HdL
30
20
10
0.2
0.4
0.6
0
0.0
0.8
SIF687 HmW m-2 sr-1 nm-1 L
0.5
1.0
1.5
2.0
SIF760 HmW m-2 sr-1 nm-1 L
Cloudy
Clear sky
Figure 7. (a–d) Scatter plots of GPP and SIF. (a,b) Half-hourly averages; (c,d) Daily averages over the
period 8:00–15:30 UTC; (a,c) GPP is plotted against red SIF in the O2 B band (SIF687); (b,d) GPP is
plotted against far red SIF in the O2 -A band (SIF760). Red (gray) points indicate clear (overcast) sky
conditions. Coefficients of determination of the linear regression between GPP and SIF and ±1σ
confidence bands are reported. All data points are considered in the regressions presented here.
Regression lines equations are : (a) 38.48x + 8.73; (b) 16.75x + 8.95; (c) 44.4x + 5.7; (d) 17.62x + 8.29.
r2
1.0
0.8
à à
à à à
à
à
à
à
æ à à æ æ
æ
0.6
æ æ æ æ
æ
æ à
æ
æ
0.4æ
à
æ
à
0.2
0.0
8
10
12
14
GPP integration time
æ
30 min
à
6.5 hours
à
à
æ æ
16
time HUTCL
Figure 8. Coefficient of determination from the linear regression between SIF760 and GPP as a function
of measurement time in clear sky conditions. Red points: GPP is averaged over a 30 min interval at the
same time as SIF760. Blue squares: GPP is averaged over the day from 8:00 to 15:30 UTC.
Remote Sens. 2017, 9, 97
16 of 31
Table 5. Statistics of the linear regression between SIF averaged over 30 min between 12:00 and 12:30
UTC and GPP as a function of experimental conditions. GPP integration time: 30 min, GPP is averaged
over 30 min between 12:00 and 12:30 UTC; 6.5 h, GPP is averaged between 8:00 and 15:30 UTC.Sky
conditions: All, all measured points at 12:00 UTC are included in the analysis; Clear sky, clear sky at
the measuring time only; Clear sky during the whole day, only data from completely clear sky days are
considered. p-value and t-statistics for the probability of the regression slope to be zero are indicated.
GPP Integration Time
30 min
30 min
30 min
6.5 h
6.5 h
6.5 h
Sky Conditions
All
Clear Sky
Clear Sky during the Whole Day
All
Clear Sky
Clear Sky during the Whole Day
data points
52
19
10
64
25
15
SIF687
r2
p-value
t-statistics
0.21
0.001
3.66
0.17
0.08
1.84
0.66
0.004
3.91
0.24
<0.001
4.41
0.2
0.02
2.42
0.66
<0.001
5.01
SIF760
r2
p-value
t-statistics
0.56
<0.001
7.9
0.56
<0.001
4.61
0.75
0.001
4.84
0.76
<0.001
13.99
0.78
<0.001
9.05
0.93
<0.001
12.99
To evaluate the effect of the crop state on the relationship between GPP and SIF, we performed
linear regressions on data from restricted time windows (Figure 9). We found noticeable changes in the
coefficient of determination (r2 ) when the regression period (20 to 70 days) was time shifted from the
beginning of crop development to senescence. Figure 9 shows the result of linear regression between
GPP and PAR. It can be seen that when a crop was under development, r2 (GPP, SIF760) was much
higher than r2 (GPP, SIF687). As a crop entered into a steady state, the relationship between SIF760
and GPP slightly weakened, and the relationship between SIF687 and GPP became stronger. When a
steady state was reached during the time window, r2 (SIF760, GPP), and r2 (SIF687, GPP) were close to
each other, and close to r2 (PAR, GPP). When the crop entered into the senescent phase, the difference
between r2 (SIF760, GPP) and r2 (SIF687, GPP) increased again, and both r2 (SIF687, GPP) and r2 (PAR,
GPP) decreased by almost the same amount. These results indicate that SIF687 does not «explain» GPP
variance better than PAR does. Similar interpretations can be drawn when considering half-hourly
data; however, with lower coefficients of determination (results not shown).
1.0
0.8
0.6
0.4
0.2
0.0
60
DOY
80 100 120 140 160
DOY
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Figure 9. Coefficient of determination (r2 ) of the linear regression between GPP and SIF760, SIF687 and
PAR for a moving time window (20, 30, 40, 50, 60, and 70 days). Linear regression was performed
between daily averages from Figure 7c,d. DOY denotes the middle of the moving time window.
3.4. Relationship between Fluorescence Yield, Photochemistry, and Light Use Efficiency
We determined the leaf response to light level by measuring the fluorescence parameters
(Fs, ∆F/F 0 m) on randomly selected leaves during the maturity stage of the crop (i.e., between DOY
118 and 143). During this period, incident PAR was almost totally absorbed by the canopy, and
Remote Sens. 2017, 9, 97
17 of 31
fAPAR remained almost constant at its maximum value with low diurnal variations (<2%, see Figure 6).
In order to compare leaf measurements to EC data, leaf data obtained with the PAM-2000 at given
times (8:30, 12:00, and 16:00) were averaged over the measured leaves to define a canopy-averaged
photochemical yield Φ P based on ∆F/F 0 m and an averaged fluorescence yield Fs (Figure 10). We found
that Fs was almost not responsive to light level in the range between 500–1800 µmol · m−2 · s−1 , and
no relationship between Fs and LUE could be evidenced (r2 = 0.11, p = 0.17). Similar results were
found by investigating the relationship between LUE and the fluorescence yield defined at canopy
level (SFY) (Table 6). Although statistically significant relationships could be found in some cases
according to the p-values, r2 values stayed low, which means that SFY can only «explain» a minor part
of LUE variation in this case. This contrasts with the relationships found between Φ P and PAR and
between Φ P and LUE, where an almost proportional relationship was found between Φ P and LUE
(Φ P = (0.053 ± 0.18) + (10.8 ± 5.8) LUE, r2 = 0.63).
F̄s (PAM-2000, r.u.)
0.4
(b)
(a)
0.3
0.2
2
2
r = 0.11
p = 0.17
r = 0.21
p = 0.04
0.1
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0.8
(c)
(d)
Φ̄P (PAM-2000)
0.6
0.4
0.2
2
2
r = 0.63
p < 0.001
r = 0.74
p < 0.001
0.0
0
500
1000
0
1500
-2
-1
PAR (µmoles photons m s )
10
20
30
40
50 60x10-3
LUE
Figure 10. Scatter plots of stationary fluorescence level (Fs) and photochemical yield (Φ P ) measured at
leaf level with a PAM-2000 as a function of incident PAR and light use efficiency from EC measurements
(LUE = GPP/APAR).r2 values of the linear regression and p-values of the slope of the regression line
are indicated. Regression line equations are (with ±99% confidence intervals): (a) y = (0.368 ± 0.055)
+(33. ± 44.) × 10−6 x, r2 = 0.21; (b) y = (0.29 ± 0.07) + (1.14 ± 2.3) x, r2 = 0.11; (c) y = (0.66 ± 0.13)
−(0.23 ± 0.10) × 10−3 x, r2 = 0.74; (d) y = (0.053 ± 0.18) + (10.8 ± 5.8) x, r2 = 0.63.
Remote Sens. 2017, 9, 97
18 of 31
Table 6. Statistics of the linear regression between half-hourly light use efficiency (LUE) and spectral
fluorescence yield (SFY) for different crop stages and sky conditions. The analysis includes data
between DOY 97 and 167 when fAPAR data is available.
Crop Stages
All
Without Senescence
Without Senescence
Sky Conditions
All
All
Clear Sky
Data points
665
531
253
687 nm
r2
p-value
0.11
<0.0001
0.16
<0.0001
0.007
0.19
760 nm
r2
p-value
0.10
<0.0001
0.006
0.06
0.05
0.0002
4. Discussion
4.1. Fluorescence Retrieval in the O2 -A and O2 -B Bands
SIF was retrieved from canopy radiance by using the principle of in-filling of oxygen absorption
bands in the radiance spectrum by fluorescence emission. Using this principle, the band profile of
the vegetation radiance spectrum was compared to the corresponding profile of a reference spectrum
acquired on a white flat panel. In the FLD (Fraunhofer Line Discrimination) method initially introduced
by Plascyk [7], substantial errors can result from the spectral variation of reflectance over the absorption
band [35,46,47]. Improved retrieval methods were designed to account for the reflectance and
fluorescence spectral shapes in the absorption bands region, such as 3FLD [48], iFLD [46], cFLD [13,49],
SFM (Spectral Fitting Method) [11,47], or SVD-based (Singular Vector Decomposition) [50]. In the
method used in this study and formerly introduced in [32], three or four spectral channels are used
to sample radiances in order to better take into account the shape of the reflectance spectrum of the
vegetation. Near the O2 -A band, reflectance varies almost linearly and, therefore, three channels are
used (3FLD). In the O2 -B band, four channels are required to characterize the curvature of reflectance
(4FLD). The performance of this method has already been tested against direct measurements of leaf
fluorescence and same values of emission ratio were found within the experimental error [35].
In Appendix A, we evaluated this method on simulated radiance spectra with known reflectance
and known fluorescence levels. In the O2 -A band, and using three channels for the retrieval,
the mean difference over the simulated database between the retrieved fluorescence and the true value
(SIF bias ) is 0.015 µmol · m−2 · sr−1 · nm−1 under an irradiance of 1400 µmol · m−2 · s−1 . It represents
about 1.2% of the experimental level observed on the fully developed crop (median value of
SIF760 is 1.3 µmol · m−2 · sr−1 · nm−1 under the same irradiance). In the total error budget, the
SIF RMSE —which represents the dispersion of the error over the simulated cases of the database
used in Appendix A—should also be considered. We cannot certify that the whole database of the
simulated cases corresponds exactly to the experimental cases that we encountered in our study, even
if we tried to use input parameters in SCOPE simulations that are as close as possible to the real ones.
However, we can consider that SIF bias ± SIF RMSE will give a good estimate of the error in most cases.
Reported to a level of 1.3 µmol · m−2 · sr−1 · nm−1 for the median value of SIF760, this error will not
exceed 3%.
In the O2 -B band, the corresponding retrieved bias is -0.038 µmol · m−2 · sr−1 · nm−1 with
four channels and 0.13 µmol · m−2 · sr−1 · nm−1 with three channels. Therefore, SIF687 is on the
average underestimated by the 4FLD method, while it is largely overestimated by the 3FLD method.
Given the lower value of SIF687 compared to SIF760, this will lead to larger relative errors on SIF687.
Reported to the median value of SIF687 retrievals of the experimental dataset at the same irradiance
level (0.5 µmol · m−2 · sr−1 · nm−1 at 1400 µmol · m−2 · s−1 ), an averaged bias of 6% is expected.
Remote Sens. 2017, 9, 97
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However, the RMSE is also greater for retrieval in the O2 -B (0.06 µmol · m−2 · sr−1 · nm−1 ). If we
consider the RMSE in the error budget, we have to acknowledge that SIF687 can be underestimated in
some cases by almost 20%.
4.2. Diurnal Cycles and Short Term Changes in Fluorescence
Leaves experience important light changes during the day or from day to day, due to change in sun
elevation, cloud cover, or occultation by other leaves. If considering other things to be equal, SIF scales
linearly with irradiance. To better assess the effect of other factors on SIF, we normalized it by PAR
to define an apparent yield fluorescence yield at top of canopy (ASFY687 and ASFY760). Light-induced
changes on SIF were evaluated on repetitive clear sky days. It was found that ASFY687 and ASFY760
slightly change during the day (by about 20% and 10%, respectively, Figure 3). Additional differences
between red and far red fluorescence were revealed when comparing diurnal cycles of SIF687
with SIF760, as well as ASFY687 with ASFY760, and when investigating the fluorescence ratio
SIF687/SIF760 (Figure 3). There is a possibility that at least a part of these differences could be
due to fluorescence retrieval errors. SIF retrievals on simulated radiances spectra have demonstrated a
RMSE of 0.06 µmol · m−2 · sr−1 · nm−1 under a PAR of 1400 µmol · m−2 · s−1 , which represents about
10–15% of SIF687. These simulation results also showed that the 4FLD algorithm tends to underestimate
SIF by about 0.038 µmol · m−2 · sr−1 · nm−1 on average. Hence, an underestimation of SIF687 in the
morning and in the evening due to retrieval errors could be a possible cause of the observed difference
between SIF687 and SIF760. Another source of errors which is not accounted for by conventional
retrieval algorithms (nFLD or SFM) is the effect of bidirectional reflectance of canopies associated
with spectral anisotropy of sky radiance. Some authors pointed out that bidirectional properties of
canopy reflectance may induce errors in SIF retrieval, depending on the difference between reflectances
under diffuse and direct lights on one hand, and on the fractions of diffuse and direct irradiances on
the other hand [51–53]. Because of different optical pathways in the atmosphere, direct and diffuse
irradiances show different spectral patterns in the oxygen bands. Specifically, the depth of the band
(defined as the ratio between the irradiance at the edge of the band and the irradiance at the bottom
of the band) is generally deeper for diffuse than for direct irradiance, because of the larger optical
pathway of diffuse light [6]. In addition, the reflectivity of the canopy could differ between direct
and diffuse light because of its complex interaction with incident light and angularly-dependent leaf
surface polarization. This would result in differences between the radiance spectra of the reference
panel and vegetation unrelated to fluorescence emission, which are a possible source of error in the
retrieved SIF signal [54]. Here, we cannot exclude a contribution of bidirectional reflectance to the
observed differences in the dynamics of SIF687 and SIF760.
However, similar differences in the diurnal patterns can be observed between TOC radiances
at wavelengths close to the SIF bands (L685 and L755). Unlike SIF, radiances are not subject to
retrieval errors. This suggests a common origin for the observed differences in the diurnal patterns
between SIF687 and SIF760, and between L685 and L755. A possible origin is the difference in the
radiative transfer within the canopy layers of the emitted/reflected–transmitted light depending on
wavelength, as red light is strongly re-absorbed by chlorophyll and far red is not. Such a pattern
with an almost linear increase (and decrease) of SIF687 has been found on pine [10], but not on
sorghum [9], which is in line with a possible effect of canopy architecture in relation with radiative
transfer. Additional experiments, using for example active measurements of fluorescence and/or
simulations with a canopy fluorescence model like SCOPE are necessary to evaluate this hypothesis.
Other phenomena (like leaf movements) could also play a role in the diurnal cycle of SIF by modifying
radiative transfer inside the canopy along the day.
On the other hand, physiological effects should also be taken into account to interpret diurnal
cycles of SIF. Red SIF originates solely from PSII, whose emission varies with photochemical and
non-photochemical quenching (PQ and NPQ, respectively), while far red SIF additionally includes
a constant PSI component. In this view, parallel changes of SIF687 and SIF760 with physiology
Remote Sens. 2017, 9, 97
20 of 31
(i.e., fluorescence quenching) are expected, with a larger amplitude for SIF687. Physiological effects
can be accounted for by active fluorescence measurements at leaf level. In this study, Fs , Fm0 , and Φ P
were assessed on leaves randomly distributed in the canopy. These measurements showed a parallel
decrease of Fm0 and Φ P at noon, which indicates an accumulation of non-photochemical quenching.
A slight but statistically significant decrease of Fs was also observed. This decrease of Fs is in good
agreement with the observed change in ASFY760, but not with the increase of ASFY687. This means
that physiological effects are masked or tightly coupled to other types of effects, such as radiative
transfer effects induced by changes in the illumination geometry. We should point out that the
approach used here to compare active and passive measurements is oversimplified, as it does not take
into account the possible interaction between the distribution of physiological effects on fluorescence
(e.g., different fluorescence yields between shaded and sunlit leaves, or between upper and lower
leaves) and the radiative transfer of incident and emitted light in the canopy. A more sophisticated
approach based on a radiative transfer modelling would be necessary to take these effects into account.
A good agreement between PAM fluorescence and far red SIF has already been observed on a maize
canopy after treatment with the herbicide DCMU [6]. Additionally, significant positive correlations
between Fs measured at leaf level and SIF760 extracted from pure crown pixels obtained by airborne
imaging spectrometer have been observed on olive and peach orchards [55] and vineyard [56].
Short term changes include changes in the distribution function of incoming light incident angles
due to aerosol content or cloud cover. It can be assessed by the relationship between apparent
fluorescence yield and the diffuse PAR fraction (Fdi f f PAR ). Here, we observe an increase of the median
value of ASFY687 by about 50% when comparing overcast sky conditions ( f di f f PAR ≥ 0.9) to clear
sky conditions ( f di f f PAR ≤ 0.3), while the corresponding change in ASFY760 does not exceed 13%.
Overcast is associated with a large decrease of PAR (from about 1600 to 500 µmol · m−2 · s−1 in this
case), which induces changes in the rate of PQ and NPQ, but we did not observe such high changes
at leaf level in Fs. Therefore, we infer that non-physiological effects are playing a role in ASFY687.
They might be linked to bidirectional reflectance (BR) effects associated with a spectral anisotropy of
irradiance that are not taken into account in the SIF retrieval algorithm and/or to changes in radiative
transfer through the canopy in link with the illumination geometry.
4.3. Seasonal Changes in Fluorescence
Long term observations (i.e., at the seasonal time scale) are characterized by large changes in
canopy structure (height, biomass, LAI) and by more stable or repetitive conditions of illumination,
if we do not consider cloudy periods. Here, we observed very different seasonal patterns between red
and far red SIF. Specifically, crop development and senescence are associated with large changes in
ASFY760, which are not seen in ASFY687 (Figure 6f,g). This is in contrast with recent results obtained
on corn crops [23]. These authors observed almost similar trends of red and far red SIF over four
seasonal cycles. A possible effect of canopy structure on the differences between red and far red
SIF cannot be ruled out, as wheat and corn have different canopy architectures. However, in their
study, a different instrumental configuration and retrieval algorithm were used, as they measured
only three channels in the O2 -B bands with a spectral resolution of 1.5 nm. In this case, the curvature
of the canopy reflectance spectrum in the O2 -B region cannot be taken into account, which would
induce retrieval errors. The differences in seasonal variations of SIF can be interpreted in terms of
different absorption foliage properties between red and far red light. When new leaves emerge and
LAI increases, red fluorescence emitted by underneath leaves is blocked by the new layer, limiting the
total red fluorescence that escapes the canopy. This is not the case of far red fluorescence, which is
scattered by leaves and have a much higher probability to escape from the canopy. In our case,
these structure-dependent effects on SIF are observed on wheat, which presents an erectophile leaf
angle distribution [38,39]. Further studies associated to a relevant modeling scheme are needed to
better characterize re-absorption effects (linked to the escape factor τ (λ)) and bidirectional reflectance
effects in the diurnal dynamic of SIF.
Remote Sens. 2017, 9, 97
21 of 31
4.4. The SIF–GPP Relationship
In our study, a significant correlation between SIF and GPP was observed, as in other
studies [16,19,20,22,23]. We observed large variations in the coefficient of determination (r2 ) of the
linear regression between SIF and GPP, according to emission wavelength, time scale of observation, sky
conditions, or phenological stage. The weakest relationship (r2 < 0.1) was observed for SIF687, and the
strongest (r2 > 0.9) for SIF760. In the regression between SIF687 and GPP, the lowest values of r2 were
obtained during the development phase. This supports the view that—unlike SIF760—SIF687 appears
to be a poor indicator of crop development, and it is also not suitable to track GPP changes induced by
an increase or decrease in photosynthetic biomass. In all cases, r2 values for SIF687 were lower than the
corresponding value for SIF760. A low correlation between red SIF and GPP has also been found on
maize in another study [23]. On leaves, a positive correlation was observed between fluorescence and
carbon assimilation, which has been attributed to increased levels of non-photochemical quenching
(NPQ) that dissipates excess energy at the photosystem II level [57]. As NPQ acts on PSII fluorescence,
one may expect a better positive correlation between photosynthesis and red fluorescence—where PSII
preferentially emits—than with far red fluorescence, which is enriched by a constant PSI fluorescence
contribution. This is not the case in our data, which suggests that other mechanisms not linked to
fluorescence quenchings control the relationship between SIF and GPP.
The highest correlation with GPP was found for SIF760 during canopy structure changes
(e.g., during development between DOYs 60–120 or senescence after DOY 150). These time periods
are characterized by rapid changes in the amount of photosynthetic biomass and the fraction of
APAR. SIF760—unlike SIF687 or reflectance indices like NDVI—has the capability to track these
seasonal changes with a high dynamic range, which is supported by data from Figure 6. This parallel
change between SIF760 and photosynthetic biomass (and hence between SIF760 and APAR) has
been previously suggested by Moya et al. [58], and was further supported by subsequent studies on
pine [10], rice [22], and sorghum [9].
In this study, we found that the stationary fluorescence yield as estimated by active measurements
at leaf level was not linked to LUE (Figure 10). We also found small variations of ASFY for a given state
of the crop, which are much lower than LUE variations. On the other hand, the photochemical
yield of PSII (Φ P ) and LUE appear to be strongly linearly correlated (r2 = 0.63, p < 0.001).
Positive correlation between the SIF normalized by APAR (SIFyield ) and LUE have been found in other
studies [23,29,59], although no proportional relationship was evidenced. This contradicts theoretical
claims that assume that under strong illumination, the ratio of LUE to fluorescence yield remains
relatively constant [21,60,61]. In any case, the lack of a proportional relationship between SIFyield (i.e.,
ASFY/fAPAR or SIF/APAR) and LUE will induce a lowering of the r2 coefficient when large variations
of LUE occur.
In our study, we found a high sensitivity of SIF760 to chlorophyll-driven changes in GPP (Figure 6)
and a low sensitivity of fluorescence yield (Fs) to changes in LUE (Figure 10). These results suggest
that the strong empirical correlation found between FRSIF and GPP in this study and in other previous
ones [16,20,22] is most likely driven by the close link between FRSIF and APAR rather than by the
recognized link between fluorescence yield and photosynthetic efficiency. This indicates that FRSIF
has limited capabilities to capture both chlorophyll content and quenching-induced changes in GPP,
contrary to what is suggested in [20]. Multiple-variable strategies should be considered instead of
a single-variable relationship. In this perspective, the photochemical reflectance index (PRI) can
bring complementary information to derive the photosynthetic efficiency component of GPP [62,63].
In another perspective, the ratio between GPP and FRSIF (SIF760 in our case) can be used to assess
the primary productivity efficiency. Data from Figure 7 shows that the slope of the regression line
between GPP and SIF760 does not change with integration time (0.5 h or 6 h). Here, it is found
to be 17.6 ± 0.9 µmolC · J−1 · sr · µm. After correction for the emission wavelength and adequately
scaled for the GPP integration time, this value is within the range of the slopes of the linear models
derived from space measurements with the TANSO instrument on board the GOSAT satellite [16,17,19],
Remote Sens. 2017, 9, 97
22 of 31
and comparable to the coefficient of the SIF-based GPP model for crops and grasslands derived from
GOME-2 data (14.9 µmolC · J−1 · sr · µm) [20]. It should be noted that these results were obtained
despite the differences in: (i) the measuring technique (atmospheric oxygen band vs. Fraunhofer lines
in the case of GOSAT); (ii) the footprint (4 m2 vs. 4◦ × 4◦ , or vs. 40 × 80 km2 ); (iii) the time scale
(daily measurement vs. monthly averages); and (iv) the measuring distance (21 m vs. around 800 km).
Hence, the GPP–FRSIF relationship appears to be robust across methods, or time and spatial scales.
This study confirms the use of ground-based studies to provide data for the development of SIF-based
GPP models that could be used in the future at larger scales. However, further work is needed to
investigate the variability of the GPP–FRSIF relationship among species and biomes, as well as its
response to stress conditions.
5. Conclusions
In this paper, we investigated diurnal and seasonal dynamics of red and far red SIF over a full
seasonal cycle on a wheat crop, and its relationship with carbon uptake assessed by eddy covariance
techniques. Using active techniques, fluorescence and PSII photochemical yield were measured at
leaf level and compared to TOC measurements. We found a low diurnal variability of the apparent
fluorescence yield (ASFY) during clear sky days, although differences between red and far red SIF
can be observed. Diurnal changes in ASFY760 are in agreement with active leaf measurements of
the mean stationary fluorescence level Fs, while ASFY687 shows variations in opposite direction.
Under cloudy sky, ASFY687 shows a noticeable increase, while ASFY760 does not change. On the
seasonal time scale, far red SIF appears to indicate better canopy growth and changes in chlorophyll
content during senescence than red SIF. We observed a strong linear relationship between far red SIF
and GPP (r2 ranging from 0.5 to >0.9, p < 0.001). This relationship is stronger during the phase of canopy
growth or changes in chlorophyll content. It is also stronger when only clear sky days are considered,
and for daily integrated values compared to 30 min integrated ones. In all cases, the relationship
between red SIF and GPP is weaker than the corresponding one between far red SIF and GPP. The
red SIF–GPP relationship is also the weakest during canopy growth. When canopy is in a steady
phenophase, SIG687 and SIF760 are almost as effective as incoming PAR in predicting GPP. In view
of these results, the use of SIF to directly assess GPP with simple linear regression presents some
limitations. We showed that the relationship between SIF and GPP is highly variable depending on the
emission wavelength, the time scale of observation, and/or the phenological stage of the crop. A part
of the loss in correlation can be attributed to re-absorption effects, as for RSIF, which poorly correlates
with GPP in all cases. Another part can be attributed to the non-proportional relationship between
Fyield and LUE that was evidenced in our study by active fluorescence measurements. Our results
suggest that using simple linear relationships to infer GPP from SIF can only be applied in specific
cases: with FRSIF, a high dynamic range of green biomass and a low variation range of LUE. In other
cases, approaches using more sophisticated algorithms that exploit the full range of the available
optical information, including RSIF, PRI (Photochemical Reflectance Index) [64,65], and reflectance
should be considered. It is important to note that these results have been obtained on a single wheat
crop over a single season. Further experiments must be conducted over multiple seasons on different
crops and species before drawing general conclusions on the relationship between SIF and GPP.
Acknowledgments: This work was supported by the “Programme National de Télédétection Spatiale” through
the “Plateforme de test pour capteurs de fluorescence satellitaires ou avionnés” project, the “Agence National
de la Recherche” (ANR) through the CALSIF project (ANR No.12-BS06-0006-01) and the CNES through the
“Terre, Océan, Surfaces Continentales, Atmosphère” (TOSCA) program (LASVEG and ACTIPASS projects).
Sébastien Champagne was granted by a CNES contract through the TOSCA program. The authors also want to
thank the “Unité Expérimentale Environnement et Agronomie d’Avignon” for the support in crop management
and measurements facilities, Jean-François Hanocq for his support during the measurement campaign and Nadine
Bertrand for the measurements of crop characteristics and data processing. We thank four anonymous reviewers
for their valuable comments.
Remote Sens. 2017, 9, 97
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Author Contributions: Ismael Moya, Fabrice Daumard, Abderrahmane Ounis and Sébastien Champagne
conceived and designed the instrumental setup. Ismael Moya and Yves Goulas conceived and designed the
experiments. Sébastien Champagne, Antoine Fournier, Abderrahmane Ounis and Yves Goulas performed
the fluorescence experiments. Olivier Marloie performed the gas exchange experiments. Yves Goulas and
Antoine Fournier analyzed the data. Yves Goulas wrote the paper with contributions and discussions from
all co-authors.
Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
Abbreviations
The following abbreviations are used in this manuscript:
ANOVA
APAR
ASFY
Chl
ChlF
DOY
EC
ESA
fAPAR
FLD
FRF
FRSIF
FWHM
GPP
LAI
LUE
NDVI
NEE
PAR
PRI
PSI, PSII
RF
RSIF
SFM
SFY
SIF
TOC
UTC
ANalysis Of VAriance
Absorbed PAR
Apparent Spectral Fluorescence Yield
Chlorophyll
Chlorophyll Fluorescence
day of the year
Eddy Covariance
European Space Agency
fraction of APAR
Fraunhofer Line Discrimination
Far Red Fluorescence
Far Red SIF
Full Width at Half-Maximum
Gross Primary Production
Leaf Area Index
Light Use Efficiency
Normalized Difference Vegetation Index
Net Ecosystem Exchange
Photosynthetically Active Radiation
Photochemical Reflectance Index
Photosystem I, Photosystem II
Red Fluorescence
Red SIF
Spectral Fitting Method
Spectral fluorescence yield
Sun-Induced Fluorescence
Top Of Canopy
Coordinated Universal Time
Appendix A. Fluorescence Retrieval
This section describes the nFLD fluorescence retrieval method used in this study and presents an
assessment of the accuracy of the retrieval on a database of simulated radiances.
Appendix A.1. Description of the Fluorescence Retrieval Method
Canopy radiance is related to irradiance and canopy fluorescence by the following equation:
L(λ) =
ρ(λ)
× I (λ) + F (λ)
π
(A1)
where I (λ), L(λ), ρ(λ), and F (λ) are, respectively, solar irradiance, canopy radiance, reflectance, and
fluorescence at wavelength λ. As reflectance varies smoothly with λ, it can be considered as constant
over channels bandwidth, and after integration over bandwidth of channel u, Equation (A1) becomes:
Remote Sens. 2017, 9, 97
24 of 31
Lu =
ρu
× Iu + Fu
π
(A2)
Fluorescence retrieval was performed according to the method described in [32]. In this method
(called nFLD hereafter, where n is the number of measuring channels), fluorescence radiance at a given
absorption band (SIF687 in the O2 -B band, SIF760 in the O2 -A band) is retrieved using a limited
number (n) of measuring channels in each O2 band. Specific models of fluorescence and reflectance
that apply only in the vicinity of each absorption band are used. The fluorescence spectrum shape
is fixed and determined by measurements at leaf level using the LSF device already described in
Section 2.2. Hence, the only free parameter for fluorescence is the overall fluorescence intensity.
Canopy reflectance is modeled with a polynomial with free coefficients whose degree is linked to the
number of measured channels. When two channels are measured, the degree is zero, which means
that reflectance is considered as constant and the method is equivalent to the simple FLD (Fraunhofer
line discrimination) method [7]. Here we used three channels in the O2 -A band, and a polynomial of
degree one to model the reflectance (i.e., a linear model). In the O2 -B band, we used four channels and
a polynomial of degree two for reflectance (i.e., a quadratic model with three parameters). With these
assumptions, the system of equations generated from Equation (A2) for all measured channels can be
exactly solved.
Appendix A.2. Performance of the Retrieval Algorithm
In order to test the performance of the method, we carried out retrieval tests on simulated
canopy radiance spectra. TOC reflectance and fluorescence spectra were generated using SCOPE
v1.60 [26]. The SCOPE model is a simulation model for radiative transfer, photosynthesis, and
energy fluxes in vegetation and soil. SCOPE has numerous input parameters to describe canopy
architecture, photosynthesis parameters, observational, and meteorological conditions. We limited
our investigations to cases that are mostly representative of the experimental conditions of the study.
Variable values were used for LAI, maximum carboxylation capacity (Vcmo), and soil spectrum
(spectrum), and combined together to produce a set of 36 fluorescence and reflectance spectra
(Figure A1). All others parameters were kept constant, and their values were determined (whenever
possible) from field measurements (Table A1). It should be noted that—despite the high values of LAI
(LAI MAX = 7) and the relative high chlorophyll content (40 µg · cm−2 ) used in the simulations—the
maximum fluorescence level is much lower than those previously found in Cogliati et al. [53] with
version v1.40 of the SCOPE model. This difference is particularly large in the red. The red peak is about
ten times that in v1.60. As irradiance levels are similar, this is probably due to the implementation of a
different leaf fluorescence model in v1.60 compared to v1.40.
0.25
0.8
Reflectance
SIF HmW m-2 sr-1 nm-1 L
1.0
0.6
0.4
0.2
0.20
0.15
0.10
0.05
0.0
0.00
650
700
750
nm
800
850
650
700
750
800
850
nm
Figure A1. SIF and reflectance spectra simulated by SCOPE with parameters depicted in Table A1.
Remote Sens. 2017, 9, 97
25 of 31
Table A1. Values of the input parameters of the SCOPE model used in the simulations.
PSII: photosystem II.
SCOPE Variable
Values
Unit
Description
PROSPECT
Cab
Cca
Cdm
Cw
Cs
N
µg · cm−2
µg · cm−2
g · cm−2
cm
fraction
40
10
0.005
0.02
0.1
1.4
Chlorophyll ab content
Carotenoid content. Usually 25% of Cab
Dry matter content
Leaf water equivalent layer
Senescent material fraction
Leaf thickness parameters
Leaf_Biochemical
Vcmo
m
Type
kV
Rdparam
Tparam
Tyear
beta
kNPQs
qLs
stressfactor
µmol · m−2 · s−1
60, 80
9
0
0.6396
0.015
0.2, 0.3, 281, 308, 328
15
0.507
0
1
1
oC
s−1
Maximum carboxylation capacity
(at optimum temperature)
Ball–Berry stomatal conductance parameter
Photochemical pathway: 0 = C3, 1 = C4
Vertical extinction coefficient of Vcmax
Respiration = Rdparam × Vcmcax
Five parameters specifying the temperature response.
Mean annual temperature
Fraction of photons partitioned to PSII
Rate constant of sustained thermal dissipation
Fraction of functional reaction centers
Stress factor to reduce Vcmax (1 = no reduction)
Fluorescence
fqe
0.01
spectrum
1, 2, 3
Fluorescence quantum yield efficiency at photosystem level
Soil
Spectrum number (column in the database soil_file)
Canopy
LAI
hc
LIDFa
LIDFb
leafwidth
0.5, 1, 2, 3, 5, 7
0.9
−1
0
0.01
m−2 · m−2
m
m
Leaf area index
Vegetation height
Leaf inclination
Variation in leaf inclination
Leaf width
Meteorological
z
Rin
Ta
p
ea
u
Ca
1
600
20
1015
13
1.77
368
tts
tto
30
0
m
W · m−2
◦C
hPa
hPa
m · s−1
ppm
Measurement height of meteorological data
Broadband incoming shortwave radiation (0.4–2.5 µm)
Air temperature
Air pressure
Atmospheric vapor pressure
Wind speed at height z
Atmospheric CO2 concentration
Angles
◦
◦
Solar zenith angle
Observation zenith angle
The solar incident irradiance spectrum was computed at a spectral resolution of 1 cm−1 from the
FLEX-S3_std.atm file, provided in the SCOPE v1.60 package. This file contains a set of 18 atmospheric
transfer functions (the so-called T1–T18 system) that are used to compute solar irradiance and
atmospheric radiances [66]. Here, we neglect the contribution of the surrounding environment in the
sky radiance. Hence, the radiance of a white Lambertian reference surface was computed as the sum
of the contributions of the direct sunlight (Esun ) and sky irradiance (Esky ), as:
LWREF =
1
( Esun + Esky ) = t1 (t4 + t5 )
π
(A3)
where ti are atmospheric transfer functions at 1 cm−1 resolution: t1 is the solar extraterrestrial
equivalent radiance, t4 the atmospheric transmittance for direct light, and t5 the diffuse scattering
function for direct light. In this case, the irradiance level corresponds approximatively to a PAR level of
Remote Sens. 2017, 9, 97
26 of 31
1400 µmol · m−2 · s−1 . The TOC radiance is computed from LWREF , canopy reflectance (ρ), and canopy
SIF (SIFc ) as:
L TOC = ρ LWREF + SIFc
(A4)
TOC and white reference spectra were subsequently convolved with the spectrometer
instrumental spectral function (ISF), and noise was added according to the noise model of the
spectrometer (Figure A2) to give simulated radiance spectra comparable to the experimental ones.
The ISF function was assessed by measuring the narrow emission of the Ar line at 686.8086 nm, which is
close to the O2 -B band. The measured spectrum was fitted by a Gaussian function (FWHM = 0.41 nm).
A slightly larger value (0.50 nm) was found for FWHM at 760 nm. The noise level of the spectrometer
was assessed by computing the standard deviation of repeated measurements (n > 500) from a halogen
source at different intensity levels. It was found that that for a given output value and after dark
correction, the noise level was independent of the wavelength channel and of the integration time. It
only depends on the output value given by the spectrometer, which is representative of the number
of photons accumulated at the detector level during the integration time. The noise was added
considering that the best signal-to-noise ratio (SNR) was achieved at the maximum of the acquired
spectrum. This means that longer integration time would be required in the O2 -B band on vegetation,
as canopy reflectance is lower in the red than in the far red.
2500
250
æ
æ
2000
æ
æ
1500
200
æ
SNR
Intensity HcountsL
æ
æ
1000
æ
ææ
150
æ
æ
æ
100
æ
æ
æ
500
0
ææææ
50
æ
æ
696.0
ææææææ
696.5
æ
697.0
0
697.5
æ
æ
æ
æ
æ
æ
0
5000
nm
10 000
counts
Figure A2. Left: Instrumental spectral function (ISF) of the spectrometer measured at the emission
wavelength of the Ar line at 686.8086 nm (full width at half-maximum (FWHM) = 0.41 nm) .
Right: Signal-to-noise ratio (SNR) as a function of intensity. The ISF function was fitted with a
2
Gaussian model as ISF (∆λ) = exp[−2 ln 2( FW∆λ
HM ) ] . SNR was fitted by a polynomial model as
6
5
4
SNR( x ) = −2867.57x + 10418.8x − 14967.1x + 10788.2x3 − 4099.82x2 + 964.652x + 1.30587, where
x is the output value of the spectrometer.
After convolution by the spectrometer ISF and the addition of noise, n channels (four in the O2 -B,
three in the O2 -A band) were extracted from the whole spectrum at wavelengths indicated in Table A2
to simulate TriFLEX measurements. To increase SNR, spectral samples were binned by computing the
mean of several spectral samples around the central wavelength of each channel.
Table A2. Positions and width of the TriFLEX channels in the O2 -A and O2 -B bands.
O2 -A
Central wavelength (nm)
Channel width (nm)
Number of spectral samples
757.86
0.5
5
760.51
0.8
8
O2 -B
770.46
0.9
9
683.14
0.5
5
685.00
0.7
7
686.97
0.1
1
697.06
0.9
9
Remote Sens. 2017, 9, 97
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This leads to the system of equations for the TriFLEX channels:

LuTOC = ρu LWREF
+ SIFu 
u





n −2
i
;
ρ u = ∑ i =0 a i λ






SIFu = Ku SIF
u ∈ [1, n]
(A5)
This system has 3n unknowns (ρu , SIFu , ai ) and 3n equations, and can be solved exactly.
The solution for SIF is given by:
L TOC
SIF =
u
∑nu=1 (−1)u LWREF
∏i< j, i6=u (λi − λ j )
u
Ku
∑nu=1 (−1)u LWREF
u
∏i< j, i6=u (λi − λ j )
(A6)
For each set of SCOPE input parameters from Table A1, we performed 30 fluorescence retrievals
in the O2 -A and O2 -B bands. This value corresponds approximatively to the number of measurements
averaged with the TriFLEX instrument in moderate light conditions at a final sampling rate of 1 min
used in this study. In high light conditions, or when SIF is averaged according to EC sampling rate
(30 min), the number of averaged samples is higher, and the final dispersion of the error is reduced.
For each simulated case, we computed the mean bias (SIFbias ), we indicated the amount of error
between the actual value and the mean retrieved value:
SIFbias =
1 30 SIFret(i) − SIFtrue
∑
30 i=1
(A7)
The mean, the median, and the root mean squared error (RMSE) were computed over the whole
database from the bias values to evaluate the retrieval method, with N being the number of simulated
cases (N = 36). Results are summarized in Table A3, with three or four channels for O2 -B and three
channels for O2 -A.
SIF bias =
∑iN=1 SIFbias
1
N
SIF median = Median (SIFbias (i )) ;
SIF RMSE =
q
1
N
∑iN=1 (SIFbias )
i ∈ [1, N ]
2
Table A3. Mean, median, and RMSE of the retrieval error on SIF at 687 nm (O2 -B) and 760 nm (O2 -A)
computed over the simulated database. Values are in µmol · m−2 · sr−1 · nm−1 and correspond to an
irradiance of approx. 1400 µmol · m−2 · s−1 .
Band
O2 -B
O2 -B
O2 -A
Number of channels
Mean bias (SIF bias )
Median bias (SIF median )
SIF RMSE
3
0.13
0.12
0.15
4
−0.038
−0.057
0.061
3
0.015
0.013
0.022
(A8)
Remote Sens. 2017, 9, 97
28 of 31
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